Basic Qualifications:
Bachelor’s degree, OR 3+ years of relevant work experience
Preferred Qualifications:
Bachelor’s degree, OR 3+ years of relevant work experience
Bachelors degree in Engineering with Honors in Data Science or Computer Science is required, along with 1+ years of hands-on experience building large scale data processing platforms
Strong understanding of data warehousing concepts, including ER data modeling, data warehouse architecture, feature engineering, and solid knowledge of the Big Data ecosystem and its 5 Vs.
1+ years of practical experience using SQL/Hive/PySpark for data extraction, aggregation, optimization, and storage on Hadoop technologies (Spark, Tez, MR) and cloud platforms
1+ years of applied GenAI engineering experience, including production grade GenAI features such as RAG over enterprise data, prompt engineering, evaluation, guardrails, and familiarity with LLMs, vectorization, chunking, and orchestration frameworks (LangChain).
Ability to build reusable components—including prompt libraries, evaluation frameworks, vector store abstractions—and integration SDKs/APIs to enable reuse across Finance scenarios.
1+ years of hands-on experience delivering end to end Business Intelligence solutions, with an understanding of ETL strategies and the ability to contribute to data model decisions for reporting.
Working knowledge of Machine Learning, Deep Learning, GenAI, and MLOps is a strong advantage.
Familiarity with Data administration (YARN, Splunk, Profiler, Perfmon, security architecture, user provisioning, audit, etc.) is preferred.
Exposure to Finance Data Analytics or finance domain is an added advantage.